We found that the number of new chemical compounds has grown exponentially with a 4.4% annual production rate from 1800 to 2015 not even affected by World Wars. There are three distinct growth regimes: proto-organic, organic, and organometallic, with decreasing variability in the production of compounds over time. Contrary to the belief that organic synthesis developed only after 1828, synthesis had been a key provider of new compounds already at the beginning of the 19th century. By 1900, it became the established tool to report new compounds. We found that chemists are conservative when selecting starting materials and that despite the growing production of new compounds, most of them belong to a restricted set of chemical compositions.
Exploration of the chemical space and its three historical regimes
Eugenio J. Llanos, Wilmer Leal, Duc H. Luu, Jürgen Jost, Peter F. Stadler, and Guillermo Restrepo
Attaching sensors to crowd-sourced vehicles could provide a cheap and accurate way to monitor air pollution, road quality, and other aspects of a city’s health. But in order for so-called drive-by sensing to be practically useful, the sensor-equipped vehicle fleet needs to have large “sensing power”—that is, it needs to cover a large fraction of a city’s area during a given reference period. Here, we provide an analytic description of the sensing power of taxi fleets, which agrees with empirical data from nine major cities. Our results show taxis’ sensing power is unexpectedly large—in Manhattan; just 10 random taxis cover one-third of street segments daily, which certifies that drive-by sensing can be readily implemented in the real world.
Quantifying the sensing power of vehicle fleets
Kevin P. O’Keeffe, Amin Anjomshoaa, Steven H. Strogatz, Paolo Santi, and Carlo Ratti
Cybersecurity is one of the fastest growing and largest technology sectors and is increasingly being recognized as one of the major issues in many industries, so companies are increasing their security budgets in order to guarantee the security of their processes. Successful menaces to the security of information systems could lead to safety, environmental, production, and quality problems.
One of the most harmful issues of attacks and intrusions is the ever-changing nature of attack technologies and strategies, which increases the difficulty of protecting computer systems. As a result, advanced systems are required to deal with the ever-increasing complexity of attacks in order to protect systems and information.
This special issue received several contributions, 5 of which have been accepted for publication.
Volume 2019, Article ID 3261453, 2 pages
Advances in Complex Systems and Their Applications to Cybersecurity
Fernando Sánchez Lasheras, Danilo Comminiello, and Alicja Krzemień
The amount of data available every day is not only enormous but growing at an exponential rate. Over the last ten years there has been an increasing interest in using complex methods to analyse and visualise massive datasets, gathered from very different sources and including many different features: social networks, surveillance systems, smart cities, medical diagnosis systems, business information, cyberphysical systems, and digital media data. Nowadays, there are a large number of researchers working in complex methods to process, analyse, and visualise all this information, which can be applied to a wide variety of open problems in different domains. This special issue presents a collection of research papers addressing theoretical, methodological, and practical aspects of data processing, focusing on algorithms that use complex methods (e.g., chaos, genetic algorithms, cellular automata, neural networks, and evolutionary game theory) in a variety of domains (e.g., software engineering, digital media data, bioinformatics, health care, imaging and video, social networks, and natural language processing). A total of 27 papers were received from different research fields, but sharing a common feature: they presented complex systems that process, analyse, and visualise large amounts of data. After the review process, 8 papers were accepted for publication (around 30% of acceptance ratio).
Volume 2019, Article ID 9316123, 2 pages
Complex Methods Applied to Data Analysis, Processing, and Visualisation
Jose Garcia-Rodriguez, Anastasia Angelopoulou, David Tomás, and Andrew Lewis
The challenge of this special issue has been to know the state of the problem related to forecasting modeling and the creation of a model to forecast the future behavior that supports decision making by supporting real-world applications.
This issue has been highlighted by the quality of its research work on the critical importance of advanced analytical methods, such as neural networks, soft computing, evolutionary algorithms, chaotic models, cellular automata, agent-based models, and finite mixture minimum squares (FIMIX-PLS)
Mainly, all the papers are focused on triggering a substantive discussion on how the model predictions can face the challenges around the complexity field that lie ahead. These works help to better understand the new trends in computing and statistical techniques that allow us to make better forecasts. Complexity plays a prominent role in these trends, given the increasing variety and changing data flows, forcing academics to adopt innovative and hybrid methods.
Volume 2019, Article ID 8160659, 3 pages
Complexity in Forecasting and Predictive Models
Jose L. Salmeron, Marisol B. Correia, and Pedro R. Palos-Sanchez